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Exposure, outcome, confounder, effect modifier●Differential vs. non-differentialMisclassification of exposure: “Differential” is with respect to the outcomeMisclassification of outcome: “Differential” is with respect to exposure
Misclassification of exposureNon-differential●You have incorrectly measured exposure, but the extent to which you have done this is the same in those with and without the outcome●Example: case-control study, exposure is measured via a biomarker, and there is systematic error in your assay. We don’t expect this to be different in cases versus controls●Effect: bias towards the nullDifferential:●Exposure ascertainment results in a different degree of measurement error in those with the outcome versus those without the outcome●Example: case-control study, exposure is measured via a biomarker, and the lab has an extra-qualified technician process the case samples●Effect: can’t predict, can go all over the place
Misclassification of outcomeNon-differential●You have incorrectly measured outcome, but the extent to which you have done this is the same in those with and without the exposure●Example: case-control study, and it’s impossible to screen all of the controls to make sure none of them are a case (e.g. this would require invasive surgery)●Effect: bias towards the null*Differential:●Outcome ascertainment results in a different degree of measurement error in the exposed versus the unexposed●Example: cohort study, exposed receive extra scrutiny●Effect: can’t predict, can go all over the place*In a follow-up study, measurement of outcome with imperfect sensitivity actually doesn’t produce bias, but it does compromise power
Misclassification of confounderAlways results in residual confounding!●Direction of bias will be the same as the direction of bias due to confounding●Your adjusted will be between the “crude” and the “truth”We don’t tend to talk about this as differential versus non-differential
MisclassificationThings to “get straight”●What are you misclassifying?●Differential vs. non-differential●Connections to sensitivity and specificitysensitivity <100%: we have falsely classified some people as not having that outcome, exposure, or confounder value, who actually dospecificity <100%: we have falsely classified some people as having that value when they do not
Why does non-differential misclassification bias towards the null?Sensitivity <100%, exposure= obesity, outcome= heart attack:●We’re putting a bunch of obese people in the non-obese group●If there is an association between obesity and heart attack, we’ll be making the non-obese people look like they have a higher risk of heart attack than they really doSpecificity <100%, exposure= obesity, outcome= heart attack:●We’re putting a bunch of non-obese people in the obese group●If there is an association, we’ll be making the obese people look like they have a lower risk of heart attack than they really do
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Type I and type II errors, Confounding, Case-control study